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RE: st: Overfitting?


From   "Kieran McCaul" <kamccaul@meddent.uwa.edu.au>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Overfitting?
Date   Sun, 15 Feb 2009 06:52:09 +0900

It's difficult to interpret this without seeing the univariate and
multivariate results for both variables.

______________________________________________
Kieran McCaul MPH PhD
WA Centre for Health & Ageing (M573)
University of Western Australia
Level 6, Ainslie House
48 Murray St
Perth 6000
Phone: (08) 9224-2140
Fax: (08) 9224 8009
email: kamccaul@meddent.uwa.edu.au
http://myprofile.cos.com/mccaul 
http://www.researcherid.com/rid/B-8751-2008
______________________________________________
The fact that no one understands you doesn't make you an artist.

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Shell makka
Sent: Saturday, 14 February 2009 6:54 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Overfitting?

Just for making it more clear in the cox model, its hazards ratio in
univariate was like:
categories      HR      CI                      P       whole p
 1           0.67 (0.56 - 0.82)	<0.001	
  2          0 .51 (0.4 - 0.67)	<0.001	
  3          0.36 (0.22 - 0.6)	<0.001	<0.001


and in the multivariate model is like:

    1        0.78 (0.63 -0.97)	0.03	
    2        0.82 (0.56 - 1.21)	0.32	
    3       0.94 (0.46 - 1.92)	0.88	0.13

So in the multivariate model the hazard is growing as year is
increasing (year categories:1, 2, 3)

Many thanks,
[Redacted]




On Sat, Feb 14, 2009 at 8:26 PM, Shell makka <shell.makka@gmail.com>
wrote:
> Thanks Maarten for your response, I will only use one of these
> covariates in my final model.
>
>
>
>
>
> On Sat, Feb 14, 2009 at 7:59 PM, Maarten buis
<maartenbuis@yahoo.co.uk> wrote:
>> --- On Sat, 14/2/09, Shell makka wrote:
>>> Would you please let me know that when effect of one
>>> covariate flips in the multivariate model, is it only
>>> because of overfitting or it can have other reasons.
>>> This variable was significant in the univariate
>>> analysis but when I used it in multivariate model which
>>> contained another covariate that these too could be
>>> also colinear (one is calander year and the other is
>>> year of starting the study) it became insignificant and
>>> its effect reversed.
>>
>> I would not call this overfitting, it just means that most
>> of the effect of calender year occurs through year of
>> starting the study. However, you do need a good theoretical
>> reason for including both variables though. It is possible
>> that such a good theoretical argument exist, but it is
>> definitely not automatic.
>>
>> -- Maarten
>>
>> -----------------------------------------
>> Maarten L. Buis
>> Department of Social Research Methodology
>> Vrije Universiteit Amsterdam
>> Boelelaan 1081
>> 1081 HV Amsterdam
>> The Netherlands
>>
>> visiting address:
>> Buitenveldertselaan 3 (Metropolitan), room N515
>>
>> +31 20 5986715
>>
>> http://home.fsw.vu.nl/m.buis/
>> -----------------------------------------
>>
>>
>>
>>
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